Normand (2001) Abstract

We determined whether adherence to recommendations for coronary angiography more than 12 h after symptom onset but prior to hospital discharge after acute myocardial infarction (AMI) resulted in better survival. Using propensity scores, we created a matched retrospective sample of 19,568 Medicare patients hospitalized with AMI during 1994–1995 in the United States. Twenty-nine percent, 36%, and 34% of patients were judged necessary, appropriate, or uncertain, respectively, for angiography while 60% of those judged necessary received the procedure during the hospitalization. The 3-year survival benefit was largest for patients rated necessary [mean survival difference (95% CI): 17.6% (15.1, 20.1)] and smallest for those rated uncertain [8.8% (6.8, 10.7)]. Angiography recommendations appear to select patients who are likely to benefit from the procedure and the consequent interventions. Because of the magnitude of the benefit and of the number of patients involved, steps should be taken to replicate these findings.

Statistical Analysis (section 2.4)

Because we collected detailed clinical information describing admission severity of the patient and characteristics of the hospital to which the patient was admitted, we assumed that treatment (angiography vs. no angiography) was randomly assigned with probabilities that depended on the observed covariates alone.

We then employed a propensity score approach to compare survival between those receiving angiography (“cathed”) and those who did not (“not cathed”) within each category of appropriateness. The propensity score is a measure of the likelihood that a patient would have undergone angiography using the patient’s covariate scores.

Creating the matched sample

To estimate the propensity scores, we fitted a logistic regression model in which the outcome was the log-odds of undergoing angiography more than 12 h after symptom onset but prior to discharge.

{The covariates used in the propensity score} consisted of patient (demographic, comorbidity, admission severity) and hospital characteristics as well as interactions among the covariates.

We assumed that missing observations were missing at random, implying that the mechanism by which data were missing is unrelated to information not contained in our observed data. For discrete-valued variables, we included a binary variable that represented “missing.” In the case of continuous-valued variables, we created two variables: a binary variable indicating whether the variable was measured and if measured, a continuous variable indicating the value of the variable.

The matched sample

Once the model was estimated, we stratified the cohort by clinical indication, and within an indication, matched each patient who underwent angiography to a patient with closest estimated propensity score who did not. We included in our analyses only those matches that were within 0.60 of the pooled standard error of q(X) where q(X) is the estimated logit. This method of defining the closeness of a match is referred to caliper matching and is the observational study analogue of randomization in a clinical trial.

Fig. 1 (next two slides) summarizes our methods for identifying and creating the matched sample.

Normand (2001) Discussion

The propensity score approach, a technique that has been employed in other recent medical studies, reduces the collection of many confounding variables to a single variable that permits easy comparisons of group differences. Although we were successful in reducing the bias that may have resulted from inexact matching on observed covariates, we were only able to adequately match 57% of all patients who underwent angiography. The unmatched angiography patients were generally younger and healthier than the matched angiography patients and if included in the comparisons would have biased the effect of angiography towards a larger benefit.

Although the exclusion of the unmatched patients may have introduced a bias, their inclusion would have also compromised the comparability of the final matched groups. Because it is difficult to completely rule out all these biases, it is important for others to validate our findings.

Normand (2001) Discussion (last paragraph)

In conclusion, coronary angiography following AMI was associated with increased survival for a relatively contemporary cohort of Medicare beneficiaries who had an AMI. The benefit was present in all categories of appropriateness that applied to these patients. Because of the magnitude of the benefit, the recent experiences of the patients, and the size of the group involved, the data suggest that not only is underuse of this procedure after AMI prevalent but may explain the lack of long-term survival differences between high-use regions and low-use regions. Because we were unable to match all patients who underwent coronary angiography, research should be undertaken to replicate our findings.

81 predictors of RHC use

Panel (7 specialists in clinical care) specified important variables related to the decision to use or not use a RHC.

• Age, Sex, Race, Education, Income, Insurance
• Primary and Secondary Disease category
• Admission diagnosis category (12 levels)
• ADL and DASI 2 weeks before admission, DNR status on day 1
• Cancer (none, local, metastasized)
• 2 month survival model prediction using baseline measures
• Weight, temperature, BP, heart rate, respiratory rate
• Comorbid illness (13 categories)
• Body chemistry (pH, WBC, PaCO\(_2\), etc.)